PhragmnÕs Voting Methods and Justified Representation
Authors: Markus Brill, Rupert Freeman, Svante Janson, Martin Lackner
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study Phragm en s methods from an axiomatic point of view, focussing on justified representation and related properties that have recently been introduced by Aziz et al. (2015a) and S anchez-Fern andez et al. (2017). We show that the sequential variant satisfies proportional justified representation, making it the first known polynomial-time computable method with this property. Moreover, we show that the optimization variants satisfy perfect representation. We also analyze the computational complexity of Phragm en s methods and provide mixed-integer programming based algorithms for computing them. |
| Researcher Affiliation | Academia | Markus Brill University of Oxford mbrill@cs.ox.ac.uk Rupert Freeman Duke University rupert@cs.duke.edu Svante Janson Uppsala University svante.janson@math.uu.se Martin Lackner University of Oxford martin.lackner@cs.ox.ac.uk |
| Pseudocode | Yes | Algorithm 1: Computing max-Phragm en |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not use datasets for empirical evaluation. The examples provided are illustrative, not empirical data. |
| Dataset Splits | No | The paper does not involve empirical experiments with datasets, and therefore no specific dataset split information (train/validation/test) is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper mentions 'mixed-integer linear and quadratic programming' as the basis for algorithms but does not specify any software names with version numbers for dependencies. |
| Experiment Setup | No | The paper is theoretical and describes algorithms and properties but does not detail an experimental setup, including specific hyperparameters or system-level training settings. |